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Found 5 papers, 4 papers with code
Meta-Task Prompting Elicits Embedding from Large Language Models
In this work, we introduce a new unsupervised embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large Language Models (LLMs) without the need for model fine-tuning or task-specific engineering.
Corpus-Steered Query Expansion with Large Language Models
Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions.
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data.
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios.
Phrase-level Textual Adversarial Attack with Label Preservation
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.
